Overview

Dataset statistics

Number of variables18
Number of observations4600
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory443.8 B

Variable types

DateTime1
Numeric10
Categorical5
Text2

Alerts

country has constant value ""Constant
price is highly overall correlated with sqft_living and 1 other fieldsHigh correlation
bedrooms is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
bathrooms is highly overall correlated with bedrooms and 4 other fieldsHigh correlation
sqft_living is highly overall correlated with price and 3 other fieldsHigh correlation
floors is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
sqft_above is highly overall correlated with price and 4 other fieldsHigh correlation
yr_built is highly overall correlated with bathrooms and 1 other fieldsHigh correlation
waterfront is highly imbalanced (93.9%)Imbalance
view is highly imbalanced (71.9%)Imbalance
price is highly skewed (γ1 = 24.79093256)Skewed
price has 49 (1.1%) zerosZeros
sqft_basement has 2745 (59.7%) zerosZeros
yr_renovated has 2735 (59.5%) zerosZeros

Reproduction

Analysis started2023-10-23 22:35:41.146406
Analysis finished2023-10-23 22:36:12.892278
Duration31.75 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

date
Date

Distinct70
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size36.1 KiB
Minimum2014-05-02 00:00:00
Maximum2014-07-10 00:00:00
2023-10-23T22:36:13.051560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:13.375047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1741
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean551962.99
Minimum0
Maximum26590000
Zeros49
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2023-10-23T22:36:14.116861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile200000
Q1322875
median460943.46
Q3654962.5
95-th percentile1184050
Maximum26590000
Range26590000
Interquartile range (IQR)332087.5

Descriptive statistics

Standard deviation563834.7
Coefficient of variation (CV)1.0215082
Kurtosis1044.3522
Mean551962.99
Median Absolute Deviation (MAD)157500
Skewness24.790933
Sum2.5390297 × 109
Variance3.1790957 × 1011
MonotonicityNot monotonic
2023-10-23T22:36:14.587076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 49
 
1.1%
300000 42
 
0.9%
400000 31
 
0.7%
440000 29
 
0.6%
450000 29
 
0.6%
600000 29
 
0.6%
350000 28
 
0.6%
250000 27
 
0.6%
435000 27
 
0.6%
415000 27
 
0.6%
Other values (1731) 4282
93.1%
ValueCountFrequency (%)
0 49
1.1%
7800 1
 
< 0.1%
80000 1
 
< 0.1%
83000 1
 
< 0.1%
83300 2
 
< 0.1%
84350 1
 
< 0.1%
87500 1
 
< 0.1%
90000 2
 
< 0.1%
100000 4
 
0.1%
102500 1
 
< 0.1%
ValueCountFrequency (%)
26590000 1
< 0.1%
12899000 1
< 0.1%
7062500 1
< 0.1%
4668000 1
< 0.1%
4489000 1
< 0.1%
3800000 1
< 0.1%
3710000 1
< 0.1%
3200000 1
< 0.1%
3100000 1
< 0.1%
3000000 1
< 0.1%

bedrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4008696
Minimum0
Maximum9
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2023-10-23T22:36:14.995289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.90884812
Coefficient of variation (CV)0.26723992
Kurtosis1.2353774
Mean3.4008696
Median Absolute Deviation (MAD)1
Skewness0.45644663
Sum15644
Variance0.8260049
MonotonicityNot monotonic
2023-10-23T22:36:15.334896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 2032
44.2%
4 1531
33.3%
2 566
 
12.3%
5 353
 
7.7%
6 61
 
1.3%
1 38
 
0.8%
7 14
 
0.3%
8 2
 
< 0.1%
0 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 38
 
0.8%
2 566
 
12.3%
3 2032
44.2%
4 1531
33.3%
5 353
 
7.7%
6 61
 
1.3%
7 14
 
0.3%
8 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
8 2
 
< 0.1%
7 14
 
0.3%
6 61
 
1.3%
5 353
 
7.7%
4 1531
33.3%
3 2032
44.2%
2 566
 
12.3%
1 38
 
0.8%
0 2
 
< 0.1%

bathrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1608152
Minimum0
Maximum8
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2023-10-23T22:36:15.640888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range8
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.78378107
Coefficient of variation (CV)0.36272471
Kurtosis1.8659047
Mean2.1608152
Median Absolute Deviation (MAD)0.5
Skewness0.61603272
Sum9939.75
Variance0.61431277
MonotonicityNot monotonic
2023-10-23T22:36:16.058863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2.5 1189
25.8%
1 743
16.2%
1.75 629
13.7%
2 427
 
9.3%
2.25 419
 
9.1%
1.5 291
 
6.3%
2.75 276
 
6.0%
3 167
 
3.6%
3.5 162
 
3.5%
3.25 136
 
3.0%
Other values (16) 161
 
3.5%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.75 17
 
0.4%
1 743
16.2%
1.25 3
 
0.1%
1.5 291
 
6.3%
1.75 629
13.7%
2 427
 
9.3%
2.25 419
 
9.1%
2.5 1189
25.8%
2.75 276
 
6.0%
ValueCountFrequency (%)
8 1
 
< 0.1%
6.75 1
 
< 0.1%
6.5 1
 
< 0.1%
6.25 2
 
< 0.1%
5.75 1
 
< 0.1%
5.5 4
 
0.1%
5.25 4
 
0.1%
5 6
 
0.1%
4.75 7
 
0.2%
4.5 29
0.6%

sqft_living
Real number (ℝ)

HIGH CORRELATION 

Distinct566
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2139.347
Minimum370
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2023-10-23T22:36:16.471719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile950
Q11460
median1980
Q32620
95-th percentile3870
Maximum13540
Range13170
Interquartile range (IQR)1160

Descriptive statistics

Standard deviation963.20692
Coefficient of variation (CV)0.45023408
Kurtosis8.2916826
Mean2139.347
Median Absolute Deviation (MAD)570
Skewness1.7235133
Sum9840996
Variance927767.56
MonotonicityNot monotonic
2023-10-23T22:36:16.842912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1940 32
 
0.7%
1720 32
 
0.7%
1660 31
 
0.7%
1840 31
 
0.7%
2000 30
 
0.7%
1410 29
 
0.6%
1200 28
 
0.6%
1480 28
 
0.6%
1700 27
 
0.6%
1490 27
 
0.6%
Other values (556) 4305
93.6%
ValueCountFrequency (%)
370 1
< 0.1%
380 1
< 0.1%
420 1
< 0.1%
430 1
< 0.1%
490 1
< 0.1%
520 1
< 0.1%
550 1
< 0.1%
560 1
< 0.1%
580 1
< 0.1%
590 2
< 0.1%
ValueCountFrequency (%)
13540 1
< 0.1%
10040 1
< 0.1%
9640 1
< 0.1%
8670 1
< 0.1%
8020 1
< 0.1%
7320 1
< 0.1%
7270 1
< 0.1%
7050 1
< 0.1%
6980 1
< 0.1%
6900 1
< 0.1%

sqft_lot
Real number (ℝ)

Distinct3113
Distinct (%)67.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14852.516
Minimum638
Maximum1074218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2023-10-23T22:36:17.104142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum638
5-th percentile1690.8
Q15000.75
median7683
Q311001.25
95-th percentile43560
Maximum1074218
Range1073580
Interquartile range (IQR)6000.5

Descriptive statistics

Standard deviation35884.436
Coefficient of variation (CV)2.416051
Kurtosis219.87299
Mean14852.516
Median Absolute Deviation (MAD)2772
Skewness11.307139
Sum68321574
Variance1.2876928 × 109
MonotonicityNot monotonic
2023-10-23T22:36:17.360029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 80
 
1.7%
6000 65
 
1.4%
4000 54
 
1.2%
7200 50
 
1.1%
4800 29
 
0.6%
4500 25
 
0.5%
9600 25
 
0.5%
3000 23
 
0.5%
5500 23
 
0.5%
7500 23
 
0.5%
Other values (3103) 4203
91.4%
ValueCountFrequency (%)
638 1
< 0.1%
681 1
< 0.1%
704 1
< 0.1%
746 1
< 0.1%
747 1
< 0.1%
750 1
< 0.1%
779 1
< 0.1%
833 1
< 0.1%
835 1
< 0.1%
844 2
< 0.1%
ValueCountFrequency (%)
1074218 1
< 0.1%
641203 1
< 0.1%
478288 1
< 0.1%
435600 2
< 0.1%
423838 1
< 0.1%
389126 1
< 0.1%
327135 1
< 0.1%
307752 1
< 0.1%
306848 1
< 0.1%
284011 1
< 0.1%

floors
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5120652
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2023-10-23T22:36:17.558783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.53828838
Coefficient of variation (CV)0.35599548
Kurtosis-0.53885198
Mean1.5120652
Median Absolute Deviation (MAD)0.5
Skewness0.55144065
Sum6955.5
Variance0.28975438
MonotonicityNot monotonic
2023-10-23T22:36:17.741959image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 2174
47.3%
2 1811
39.4%
1.5 444
 
9.7%
3 128
 
2.8%
2.5 41
 
0.9%
3.5 2
 
< 0.1%
ValueCountFrequency (%)
1 2174
47.3%
1.5 444
 
9.7%
2 1811
39.4%
2.5 41
 
0.9%
3 128
 
2.8%
3.5 2
 
< 0.1%
ValueCountFrequency (%)
3.5 2
 
< 0.1%
3 128
 
2.8%
2.5 41
 
0.9%
2 1811
39.4%
1.5 444
 
9.7%
1 2174
47.3%

waterfront
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size260.7 KiB
0
4567 
1
 
33

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4600
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4567
99.3%
1 33
 
0.7%

Length

2023-10-23T22:36:17.931437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-23T22:36:18.126462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 4567
99.3%
1 33
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 4567
99.3%
1 33
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4600
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4567
99.3%
1 33
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 4600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4567
99.3%
1 33
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4567
99.3%
1 33
 
0.7%

view
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size260.7 KiB
0
4140 
2
 
205
3
 
116
4
 
70
1
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4600
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row4
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4140
90.0%
2 205
 
4.5%
3 116
 
2.5%
4 70
 
1.5%
1 69
 
1.5%

Length

2023-10-23T22:36:18.278606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-23T22:36:18.494775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 4140
90.0%
2 205
 
4.5%
3 116
 
2.5%
4 70
 
1.5%
1 69
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 4140
90.0%
2 205
 
4.5%
3 116
 
2.5%
4 70
 
1.5%
1 69
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4600
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4140
90.0%
2 205
 
4.5%
3 116
 
2.5%
4 70
 
1.5%
1 69
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 4600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4140
90.0%
2 205
 
4.5%
3 116
 
2.5%
4 70
 
1.5%
1 69
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4140
90.0%
2 205
 
4.5%
3 116
 
2.5%
4 70
 
1.5%
1 69
 
1.5%

condition
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size260.7 KiB
3
2875 
4
1252 
5
435 
2
 
32
1
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4600
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row5
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
3 2875
62.5%
4 1252
27.2%
5 435
 
9.5%
2 32
 
0.7%
1 6
 
0.1%

Length

2023-10-23T22:36:18.685510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-23T22:36:18.893566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 2875
62.5%
4 1252
27.2%
5 435
 
9.5%
2 32
 
0.7%
1 6
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 2875
62.5%
4 1252
27.2%
5 435
 
9.5%
2 32
 
0.7%
1 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4600
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2875
62.5%
4 1252
27.2%
5 435
 
9.5%
2 32
 
0.7%
1 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4600
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2875
62.5%
4 1252
27.2%
5 435
 
9.5%
2 32
 
0.7%
1 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4600
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2875
62.5%
4 1252
27.2%
5 435
 
9.5%
2 32
 
0.7%
1 6
 
0.1%

sqft_above
Real number (ℝ)

HIGH CORRELATION 

Distinct511
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1827.2654
Minimum370
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2023-10-23T22:36:19.106418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile860
Q11190
median1590
Q32300
95-th percentile3440
Maximum9410
Range9040
Interquartile range (IQR)1110

Descriptive statistics

Standard deviation862.16898
Coefficient of variation (CV)0.47183565
Kurtosis4.0701383
Mean1827.2654
Median Absolute Deviation (MAD)490
Skewness1.4942107
Sum8405421
Variance743335.34
MonotonicityNot monotonic
2023-10-23T22:36:19.340845image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1200 47
 
1.0%
1010 47
 
1.0%
1300 45
 
1.0%
1140 44
 
1.0%
1320 43
 
0.9%
1150 42
 
0.9%
1090 40
 
0.9%
1180 40
 
0.9%
1400 38
 
0.8%
1050 37
 
0.8%
Other values (501) 4177
90.8%
ValueCountFrequency (%)
370 1
 
< 0.1%
380 1
 
< 0.1%
420 1
 
< 0.1%
430 1
 
< 0.1%
490 1
 
< 0.1%
520 1
 
< 0.1%
550 3
0.1%
560 1
 
< 0.1%
580 1
 
< 0.1%
590 2
< 0.1%
ValueCountFrequency (%)
9410 1
< 0.1%
8020 1
< 0.1%
7680 1
< 0.1%
7320 1
< 0.1%
6640 1
< 0.1%
6430 1
< 0.1%
6420 1
< 0.1%
6120 1
< 0.1%
6070 1
< 0.1%
6050 1
< 0.1%

sqft_basement
Real number (ℝ)

ZEROS 

Distinct207
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean312.08152
Minimum0
Maximum4820
Zeros2745
Zeros (%)59.7%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2023-10-23T22:36:19.580019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3610
95-th percentile1210
Maximum4820
Range4820
Interquartile range (IQR)610

Descriptive statistics

Standard deviation464.13723
Coefficient of variation (CV)1.4872307
Kurtosis4.08238
Mean312.08152
Median Absolute Deviation (MAD)0
Skewness1.6427322
Sum1435575
Variance215423.37
MonotonicityNot monotonic
2023-10-23T22:36:19.829887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2745
59.7%
500 53
 
1.2%
600 45
 
1.0%
800 43
 
0.9%
900 41
 
0.9%
700 38
 
0.8%
1000 33
 
0.7%
400 33
 
0.7%
550 27
 
0.6%
750 26
 
0.6%
Other values (197) 1516
33.0%
ValueCountFrequency (%)
0 2745
59.7%
20 1
 
< 0.1%
50 1
 
< 0.1%
60 2
 
< 0.1%
65 1
 
< 0.1%
70 1
 
< 0.1%
80 3
 
0.1%
90 2
 
< 0.1%
100 14
 
0.3%
110 2
 
< 0.1%
ValueCountFrequency (%)
4820 1
< 0.1%
4130 1
< 0.1%
2850 1
< 0.1%
2730 1
< 0.1%
2550 2
< 0.1%
2360 1
< 0.1%
2330 1
< 0.1%
2300 1
< 0.1%
2200 1
< 0.1%
2180 1
< 0.1%

yr_built
Real number (ℝ)

HIGH CORRELATION 

Distinct115
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.7863
Minimum1900
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2023-10-23T22:36:20.066983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1913
Q11951
median1976
Q31997
95-th percentile2009
Maximum2014
Range114
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.731848
Coefficient of variation (CV)0.015086287
Kurtosis-0.6700759
Mean1970.7863
Median Absolute Deviation (MAD)23
Skewness-0.50215519
Sum9065617
Variance883.98281
MonotonicityNot monotonic
2023-10-23T22:36:20.335417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2006 111
 
2.4%
2005 104
 
2.3%
2007 93
 
2.0%
2004 92
 
2.0%
1978 90
 
2.0%
2003 89
 
1.9%
2008 89
 
1.9%
1967 82
 
1.8%
1977 80
 
1.7%
2014 78
 
1.7%
Other values (105) 3692
80.3%
ValueCountFrequency (%)
1900 22
0.5%
1901 9
 
0.2%
1902 10
 
0.2%
1903 10
 
0.2%
1904 9
 
0.2%
1905 19
0.4%
1906 27
0.6%
1907 12
0.3%
1908 19
0.4%
1909 22
0.5%
ValueCountFrequency (%)
2014 78
1.7%
2013 57
1.2%
2012 33
 
0.7%
2011 24
 
0.5%
2010 28
 
0.6%
2009 50
1.1%
2008 89
1.9%
2007 93
2.0%
2006 111
2.4%
2005 104
2.3%

yr_renovated
Real number (ℝ)

ZEROS 

Distinct60
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean808.60826
Minimum0
Maximum2014
Zeros2735
Zeros (%)59.5%
Negative0
Negative (%)0.0%
Memory size36.1 KiB
2023-10-23T22:36:20.592082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31999
95-th percentile2011
Maximum2014
Range2014
Interquartile range (IQR)1999

Descriptive statistics

Standard deviation979.41454
Coefficient of variation (CV)1.2112349
Kurtosis-1.8511109
Mean808.60826
Median Absolute Deviation (MAD)0
Skewness0.3859187
Sum3719598
Variance959252.83
MonotonicityNot monotonic
2023-10-23T22:36:20.850601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2735
59.5%
2000 170
 
3.7%
2003 151
 
3.3%
2009 109
 
2.4%
2001 109
 
2.4%
2005 95
 
2.1%
2004 77
 
1.7%
2014 72
 
1.6%
2006 68
 
1.5%
2013 61
 
1.3%
Other values (50) 953
 
20.7%
ValueCountFrequency (%)
0 2735
59.5%
1912 33
 
0.7%
1913 1
 
< 0.1%
1923 57
 
1.2%
1934 6
 
0.1%
1945 7
 
0.2%
1948 1
 
< 0.1%
1953 1
 
< 0.1%
1954 8
 
0.2%
1955 2
 
< 0.1%
ValueCountFrequency (%)
2014 72
1.6%
2013 61
1.3%
2012 45
1.0%
2011 54
1.2%
2010 30
 
0.7%
2009 109
2.4%
2008 45
1.0%
2007 7
 
0.2%
2006 68
1.5%
2005 95
2.1%

street
Text

Distinct4525
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size332.6 KiB
2023-10-23T22:36:21.270197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length46
Median length40
Mean length17.018261
Min length8

Characters and Unicode

Total characters78284
Distinct characters62
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4453 ?
Unique (%)96.8%

Sample

1st row18810 Densmore Ave N
2nd row709 W Blaine St
3rd row26206-26214 143rd Ave SE
4th row857 170th Pl NE
5th row9105 170th Ave NE
ValueCountFrequency (%)
ave 1940
 
10.5%
ne 1358
 
7.4%
se 1180
 
6.4%
st 1171
 
6.3%
pl 807
 
4.4%
s 562
 
3.0%
sw 513
 
2.8%
n 292
 
1.6%
nw 288
 
1.6%
ct 173
 
0.9%
Other values (4805) 10157
55.1%
2023-10-23T22:36:21.986150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13841
17.7%
1 5952
 
7.6%
t 4628
 
5.9%
2 4618
 
5.9%
S 3522
 
4.5%
0 3119
 
4.0%
3 3090
 
3.9%
e 2891
 
3.7%
h 2787
 
3.6%
4 2775
 
3.5%
Other values (52) 31061
39.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29557
37.8%
Lowercase Letter 20844
26.6%
Space Separator 13841
17.7%
Uppercase Letter 13697
17.5%
Dash Punctuation 343
 
0.4%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 4628
22.2%
e 2891
13.9%
h 2787
13.4%
v 2044
9.8%
l 1311
 
6.3%
r 1115
 
5.3%
n 1066
 
5.1%
d 1000
 
4.8%
a 907
 
4.4%
s 589
 
2.8%
Other values (15) 2506
12.0%
Uppercase Letter
ValueCountFrequency (%)
S 3522
25.7%
E 2718
19.8%
A 2018
14.7%
N 1966
14.4%
W 1183
 
8.6%
P 895
 
6.5%
C 283
 
2.1%
D 184
 
1.3%
L 150
 
1.1%
M 148
 
1.1%
Other values (14) 630
 
4.6%
Decimal Number
ValueCountFrequency (%)
1 5952
20.1%
2 4618
15.6%
0 3119
10.6%
3 3090
10.5%
4 2775
9.4%
5 2409
8.2%
6 2060
 
7.0%
7 1928
 
6.5%
8 1849
 
6.3%
9 1757
 
5.9%
Space Separator
ValueCountFrequency (%)
13841
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 343
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43743
55.9%
Latin 34541
44.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 4628
13.4%
S 3522
 
10.2%
e 2891
 
8.4%
h 2787
 
8.1%
E 2718
 
7.9%
v 2044
 
5.9%
A 2018
 
5.8%
N 1966
 
5.7%
l 1311
 
3.8%
W 1183
 
3.4%
Other values (39) 9473
27.4%
Common
ValueCountFrequency (%)
13841
31.6%
1 5952
13.6%
2 4618
 
10.6%
0 3119
 
7.1%
3 3090
 
7.1%
4 2775
 
6.3%
5 2409
 
5.5%
6 2060
 
4.7%
7 1928
 
4.4%
8 1849
 
4.2%
Other values (3) 2102
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 78284
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13841
17.7%
1 5952
 
7.6%
t 4628
 
5.9%
2 4618
 
5.9%
S 3522
 
4.5%
0 3119
 
4.0%
3 3090
 
3.9%
e 2891
 
3.7%
h 2787
 
3.6%
4 2775
 
3.5%
Other values (52) 31061
39.7%

city
Categorical

Distinct44
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size291.0 KiB
Seattle
1573 
Renton
293 
Bellevue
286 
Redmond
235 
Issaquah
 
187
Other values (39)
2026 

Length

Max length19
Median length18
Mean length7.753913
Min length4

Characters and Unicode

Total characters35668
Distinct characters45
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st rowShoreline
2nd rowSeattle
3rd rowKent
4th rowBellevue
5th rowRedmond

Common Values

ValueCountFrequency (%)
Seattle 1573
34.2%
Renton 293
 
6.4%
Bellevue 286
 
6.2%
Redmond 235
 
5.1%
Issaquah 187
 
4.1%
Kirkland 187
 
4.1%
Kent 185
 
4.0%
Auburn 176
 
3.8%
Sammamish 175
 
3.8%
Federal Way 148
 
3.2%
Other values (34) 1155
25.1%

Length

2023-10-23T22:36:22.256595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
seattle 1573
30.4%
renton 293
 
5.7%
bellevue 286
 
5.5%
redmond 235
 
4.5%
issaquah 187
 
3.6%
kirkland 187
 
3.6%
kent 185
 
3.6%
auburn 176
 
3.4%
sammamish 175
 
3.4%
federal 148
 
2.9%
Other values (47) 1722
33.3%

Most occurring characters

ValueCountFrequency (%)
e 6423
18.0%
t 3861
10.8%
l 3602
 
10.1%
a 3573
 
10.0%
n 2261
 
6.3%
S 1975
 
5.5%
o 1382
 
3.9%
r 1137
 
3.2%
d 1113
 
3.1%
i 1079
 
3.0%
Other values (35) 9262
26.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29903
83.8%
Uppercase Letter 5197
 
14.6%
Space Separator 567
 
1.6%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6423
21.5%
t 3861
12.9%
l 3602
12.0%
a 3573
11.9%
n 2261
 
7.6%
o 1382
 
4.6%
r 1137
 
3.8%
d 1113
 
3.7%
i 1079
 
3.6%
u 1071
 
3.6%
Other values (14) 4401
14.7%
Uppercase Letter
ValueCountFrequency (%)
S 1975
38.0%
R 535
 
10.3%
B 453
 
8.7%
K 438
 
8.4%
I 274
 
5.3%
W 263
 
5.1%
M 253
 
4.9%
F 196
 
3.8%
A 182
 
3.5%
V 126
 
2.4%
Other values (9) 502
 
9.7%
Space Separator
ValueCountFrequency (%)
567
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 35100
98.4%
Common 568
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6423
18.3%
t 3861
11.0%
l 3602
10.3%
a 3573
10.2%
n 2261
 
6.4%
S 1975
 
5.6%
o 1382
 
3.9%
r 1137
 
3.2%
d 1113
 
3.2%
i 1079
 
3.1%
Other values (33) 8694
24.8%
Common
ValueCountFrequency (%)
567
99.8%
- 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35668
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6423
18.0%
t 3861
10.8%
l 3602
 
10.1%
a 3573
 
10.0%
n 2261
 
6.3%
S 1975
 
5.5%
o 1382
 
3.9%
r 1137
 
3.2%
d 1113
 
3.1%
i 1079
 
3.0%
Other values (35) 9262
26.0%
Distinct77
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size292.1 KiB
2023-10-23T22:36:22.592921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters36800
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowWA 98133
2nd rowWA 98119
3rd rowWA 98042
4th rowWA 98008
5th rowWA 98052
ValueCountFrequency (%)
wa 4600
50.0%
98103 148
 
1.6%
98052 135
 
1.5%
98117 132
 
1.4%
98115 130
 
1.4%
98006 110
 
1.2%
98059 106
 
1.2%
98042 100
 
1.1%
98034 99
 
1.1%
98074 98
 
1.1%
Other values (68) 3542
38.5%
2023-10-23T22:36:23.161973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 5274
14.3%
9 5201
14.1%
W 4600
12.5%
A 4600
12.5%
4600
12.5%
0 3695
10.0%
1 2741
7.4%
5 1275
 
3.5%
2 1244
 
3.4%
3 1146
 
3.1%
Other values (3) 2424
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23000
62.5%
Uppercase Letter 9200
 
25.0%
Space Separator 4600
 
12.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 5274
22.9%
9 5201
22.6%
0 3695
16.1%
1 2741
11.9%
5 1275
 
5.5%
2 1244
 
5.4%
3 1146
 
5.0%
7 876
 
3.8%
4 778
 
3.4%
6 770
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
W 4600
50.0%
A 4600
50.0%
Space Separator
ValueCountFrequency (%)
4600
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27600
75.0%
Latin 9200
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 5274
19.1%
9 5201
18.8%
4600
16.7%
0 3695
13.4%
1 2741
9.9%
5 1275
 
4.6%
2 1244
 
4.5%
3 1146
 
4.2%
7 876
 
3.2%
4 778
 
2.8%
Latin
ValueCountFrequency (%)
W 4600
50.0%
A 4600
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 36800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 5274
14.3%
9 5201
14.1%
W 4600
12.5%
A 4600
12.5%
4600
12.5%
0 3695
10.0%
1 2741
7.4%
5 1275
 
3.5%
2 1244
 
3.4%
3 1146
 
3.1%
Other values (3) 2424
6.6%

country
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size269.7 KiB
USA
4600 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13800
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSA
2nd rowUSA
3rd rowUSA
4th rowUSA
5th rowUSA

Common Values

ValueCountFrequency (%)
USA 4600
100.0%

Length

2023-10-23T22:36:23.446721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-23T22:36:23.647191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
usa 4600
100.0%

Most occurring characters

ValueCountFrequency (%)
U 4600
33.3%
S 4600
33.3%
A 4600
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13800
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 4600
33.3%
S 4600
33.3%
A 4600
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 13800
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 4600
33.3%
S 4600
33.3%
A 4600
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 4600
33.3%
S 4600
33.3%
A 4600
33.3%

Interactions

2023-10-23T22:36:09.283077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:44.999058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:47.977146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:50.064799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:52.510901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:54.742028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:56.846510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:59.972305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:02.971734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:05.930700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:09.648983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:45.297129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:48.180577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:50.301028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:52.747775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:54.948538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:57.050572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:00.301282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:03.176710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:06.261767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:09.855077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:45.625005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:48.366950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:50.529585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:52.954246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:55.144358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:57.241799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:00.613600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:03.363592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:06.584150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:10.120234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:45.981207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:48.589242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:50.770512image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:53.210022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:55.365517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:57.576524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:00.976347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:03.589985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:06.954057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:10.350399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:46.350694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:48.810916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:51.011104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:53.448401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:55.587228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:57.931941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:01.627056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:03.970671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:07.298609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:10.566051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:46.699630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:49.018435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:51.240371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:53.663830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:55.799221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:58.270952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:01.958192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:04.175525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:07.622872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:10.796594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:47.050354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:49.219579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:51.461299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:53.884830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:56.010859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:58.601272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:02.153394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:04.393267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:07.962727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:10.993298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:47.332194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:49.431664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:51.667650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:54.080853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:56.201242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:58.930931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:02.334080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:04.748852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:08.283381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:11.216584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:47.537228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:49.638144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:52.049343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:54.295552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:56.399638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:59.259375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:02.535248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:05.027446image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:08.607405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:11.428115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:47.747647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:49.834990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:52.269383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:54.513703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:56.615548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:35:59.602438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:02.752646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:05.494844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-23T22:36:08.959227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-23T22:36:23.796945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
pricebedroomsbathroomssqft_livingsqft_lotfloorssqft_abovesqft_basementyr_builtyr_renovatedwaterfrontviewconditioncity
price1.0000.3380.4920.6310.0750.3210.5340.2370.084-0.0710.2260.0940.0000.114
bedrooms0.3381.0000.5380.6520.2380.2200.5330.2480.160-0.0560.0000.0860.0660.098
bathrooms0.4920.5381.0000.7470.0920.5400.6960.1900.530-0.2130.1660.1460.1290.145
sqft_living0.6310.6520.7471.0000.3250.3970.8430.3230.322-0.1270.2690.1730.0460.148
sqft_lot0.0750.2380.0920.3251.000-0.2040.3050.023-0.0120.0510.0000.0490.0520.185
floors0.3210.2200.5400.397-0.2041.0000.604-0.2880.538-0.2290.0000.0330.1860.190
sqft_above0.5340.5330.6960.8430.3050.6041.000-0.1720.460-0.1690.1340.1020.1080.180
sqft_basement0.2370.2480.1900.3230.023-0.288-0.1721.000-0.2120.0540.2110.1950.1170.113
yr_built0.0840.1600.5300.322-0.0120.5380.460-0.2121.000-0.3150.0260.0550.2650.267
yr_renovated-0.071-0.056-0.213-0.1270.051-0.229-0.1690.054-0.3151.0000.0000.0500.2170.213
waterfront0.2260.0000.1660.2690.0000.0000.1340.2110.0260.0001.0000.4830.0000.234
view0.0940.0860.1460.1730.0490.0330.1020.1950.0550.0500.4831.0000.0270.113
condition0.0000.0660.1290.0460.0520.1860.1080.1170.2650.2170.0000.0271.0000.131
city0.1140.0980.1450.1480.1850.1900.1800.1130.2670.2130.2340.1130.1311.000

Missing values

2023-10-23T22:36:11.794234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-23T22:36:12.553673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

datepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditionsqft_abovesqft_basementyr_builtyr_renovatedstreetcitystatezipcountry
02014-05-02 00:00:00313000.03.01.50134079121.5003134001955200518810 Densmore Ave NShorelineWA 98133USA
12014-05-02 00:00:002384000.05.02.50365090502.0045337028019210709 W Blaine StSeattleWA 98119USA
22014-05-02 00:00:00342000.03.02.001930119471.0004193001966026206-26214 143rd Ave SEKentWA 98042USA
32014-05-02 00:00:00420000.03.02.25200080301.00041000100019630857 170th Pl NEBellevueWA 98008USA
42014-05-02 00:00:00550000.04.02.501940105001.00041140800197619929105 170th Ave NERedmondWA 98052USA
52014-05-02 00:00:00490000.02.01.0088063801.0003880019381994522 NE 88th StSeattleWA 98115USA
62014-05-02 00:00:00335000.02.02.00135025601.000313500197602616 174th Ave NERedmondWA 98052USA
72014-05-02 00:00:00482000.04.02.502710358682.0003271001989023762 SE 253rd PlMaple ValleyWA 98038USA
82014-05-02 00:00:00452500.03.02.502430884261.000415708601985046611-46625 SE 129th StNorth BendWA 98045USA
92014-05-02 00:00:00640000.04.02.00152062001.500315200194520106811 55th Ave NESeattleWA 98115USA
datepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditionsqft_abovesqft_basementyr_builtyr_renovatedstreetcitystatezipcountry
45902014-07-08 00:00:00380680.5555564.02.50262083312.0003262001991013602 SE 186th PlRentonWA 98058USA
45912014-07-08 00:00:00396166.6666673.01.75188057521.0004940940194503529 SW Webster StSeattleWA 98126USA
45922014-07-08 00:00:00252980.0000004.02.50253081692.0003253001993037654 18th Pl SFederal WayWA 98003USA
45932014-07-08 00:00:00289373.3076923.02.50253846002.000325380201319235703 Charlotte Ave SEAuburnWA 98092USA
45942014-07-09 00:00:00210614.2857143.02.50161072232.0003161001994026306 127th Ave SEKentWA 98030USA
45952014-07-09 00:00:00308166.6666673.01.75151063601.00041510019541979501 N 143rd StSeattleWA 98133USA
45962014-07-09 00:00:00534333.3333333.02.50146075732.0003146001983200914855 SE 10th PlBellevueWA 98007USA
45972014-07-09 00:00:00416904.1666673.02.50301070142.00033010020090759 Ilwaco Pl NERentonWA 98059USA
45982014-07-10 00:00:00203400.0000004.02.00209066301.000310701020197405148 S Creston StSeattleWA 98178USA
45992014-07-10 00:00:00220600.0000003.02.50149081022.0004149001990018717 SE 258th StCovingtonWA 98042USA